DSS 2.3 Relase notes¶
For automatic upgrade information, see Upgrading a DSS instance
Migration to DSS 2.3 from DSS 1.X is not supported. You should first migrate to the latest 2.0.X version. See DSS 2.0 Relase notes
Automatic migration from Data Science Studio 2.2.X and 2.1.X is supported, with the following restrictions and warnings:
The usual limitations on retraining models and regenerating API node packages apply (see Upgrading a DSS instance for more information)
Automatic migration from Data Science Studio 2.0.X is supported, with the previous restrictions and warnings, and, in addition, the ones outlined in DSS 2.1 Relase notes
DSS 2.3.5 is a bugfix and minor enhancements release. For a summary of new features in DSS 2.3.0, see below.
Preserve the “hive.query.string” Hadoop configuration key in Hive notebook
Clear error message when trying to use Geometry columns in Spark
Fix wrongly computed multiclass metrics
Much faster multiclass scoring for MLLib
Fix multiclass AUC when only 2 classes appear in test set
Fix tooltip issues in the clustering scatter plot
Fix typo in custom HTTP header that could lead to inability to parse the response
Fix the INSEE enrichment processor
Fix excessive verbosity
Fix DateParser in multi-columns mode when some of the columns are empty
Modifying a step comment now properly unlocks the “Save” button
Fix split recipe on “exotic” boolean values (Yes, No, 1, 0, …)
Add percentage mode on pie/donut chart
Enforce hierarchy of files to prevent possible out-of-datadir reads
Fix support for nginx >= 1.10
DSS 2.3.4 is a bugfix and minor enhancements release. For a summary of new features in DSS 2.3.0, see below.
Add support for CDH 5.7 and Hive-on-Spark
Fix “flag” processorso perating on “columns pattern” mode
Fix a UI issue with date facets
Add a few missing country names
Fix modulo operator on large numericals (> 2^31)
Window recipe: fix typing of custom aggregations
Fix an issue that could lead to Abort not working properly on jobs
DSS 2.3.3 is a bugfix release. For a summary of new features in DSS 2.3.0, see below.
Fixed support for Hive in MapR-ecosystem versions 1601 and above
Added support for Spark 1.6
``select now()`in Impala notebook
Fixed misinterpretation of numbers like “017” as octal in the “Nest” processor
The variables will now be interpreted in the context of the current preparation, not the context of the input dataset
Fixed UI flickering
Fixed wrong “dirty” (i.e. not saved) state in the UI when there is a group in the recipe
Fixed bad state for the “prefix” option of the Unnest processor
SQL recipe: Fixed conversion from SQL notebook
Window recipe: Fixed data types for cumulative distribution
Stack recipe: Fixed custom schema definition in Stack recipe
Stack recipe: Fixed postfiler on filesystem-based datasets
Join recipe: Fixed “join on string contains” on Greenplum
Join recipe: Fixed computed date columns on Oracle
Join recipe: Fixed possible issue with cross-project joins on Hive
Filter recipe: Fixed boolean conditions on filesystem-based datasets
Group recipe: Fixed grouping in stream engine with empty values in integer columns
Group recipe: Fixed grouping on custom key in stream engine
Group recipe: Fixed UI issue when removing all grouping keys
All visual recipes: Fixed filters in multi-word column names
Sync recipe: Fixed partitioned to non-partitioned dataset creation
All recipes: Fixed UI for the time-range partitions dependency
`iter_tuples(columns=)`which did not take columns into account
Fixed mishandling of accentuated values, leading them to appear as “Others” in dummy-encoded columns
Fixed clustering scoring recipe if no name was given to any cluster
Fixed wrong description of the metric used for classification threshold optimization
Fixed possible migration / settings issue in Ridge regression
Fixed tags flow view in projects with foreign datasets
Fixed export of dataframe from Jupyter noteboook
Fixed user/group dialogs in LDAP mode
Fixed an occasional deadlock
DSS 2.3.2 is a bugfix release. For a summary of new features in DSS 2.3.0, see below.
Spark engine: Add support for probabilities in Random forest
Spark engine: Improve stability of results for models
Spark engine: Fix casting of output integers to doubles
Python engine: Fix a code error in Jupyter notebook export
Fix fuzzy join hanging in rare conditions
Fix custom Python steps when there are custom variables with quotes
Fix deploying analysis on other dataset
Split: Fix support for adding custom variables to output dataset
Split: Fix UI reloading that could lead to broken recipe
Stack: Fix on Spark engine
Fix occasional issue with “Publish” button on Firefox
Fix support for filterExpression in JS API
Fix export of non-partitioned dataset after export of partitioned dataset with explicit partitions list
Small UI fixes
DSS 2.3.1 is a bugfix release. For a summary of new features in DSS 2.3.0, see below.
Disable IPv6 listening (introduced in 2.3.0) by default
Fix RemoteFiles dataset
Fix running on Hadoop and Spark with “Remove rows on bad meaning” processor
Fix a case where the data quality bars were not properly updated
Fix formatting issue in latitude/longitude for GeoIP processor
Add a few missing countries to the Countries detector
Default to repartitioning non-HDFS datasets in fewer partitions to avoid consuming too many file handles
Fix some prefix search cases with uppercase identifiers
Fixed filtering of features in settings screens
Don’t show “Export notebook” option for MLLib models
Hide useless size parameter for filled admin maps
Show tabs explicitly in code editors
Add code samples for webapps
DSS 2.3.0 is a major upgrade to DSS with exciting improvements.
For a summary of the major new features, see: https://www.dataiku.com/learn/whatsnew
Visual data preparation, reloaded¶
Our visual data preparation component is a major focus of DSS 2.3, with numerous large improvements:
You can now group, color and comment all script steps
It is now possible to color the cells of the data table by their values, to easily locate co-occurences, or columns changing together.
The Python and Formula editors are now much easier to use.
The Formula language has been strongly enriched, making it a very powerful, but still very easy to use tool. See Formula language and our new Howto for more information.
The Quick Column View provides an immediate overview of all columns, and allows you to navigate very simply across columns.
The header at the top of the table now always precisely tells you what you are seing and the impact of your preparation on your data.
Many processors are now multi-column able, including the ability to select all columns, or all columns matching a pattern.
It is now possible to temporarily hide some columns
Hit Shift+V to view “long” values in a data table cell, including JSON formatting and folding
The redesigned UI makes it much clearer to navigate your preparation steps and data table.
Schemas edition and user-defined meanings¶
If it now possible to edit the schemas of datasets directly in the Exploration screen.
You can now define your own meanings, either declaratively, through values lists or through patterns. User-defined meanings are available everywhere you need them, bringing easy documentation to your data projects. For more informations, see Schemas, storage types and meanings
Since the very first versions, DSS let you search within your project. Thanks to the new Data Catalog, you now have an extremely powerful instance-wide search. Even if you have dozens of projects, you’ll be able to find easily all DSS objects, with deep search (search a dataset by column name, a recipe by comments in the code, …).
The Data Catalog provides a faceted search and fully respects applicative security.
Flow tools and views¶
The new Flow views system is an advanced productivity tool, especially designed for large projects. It provides additional “layers” onto the Flow:
Color by tag name and easily propagate tags
Recursively check and propagate schema changes across a Flow
Check the consistency of datasets and recipes across a project.
New SQL / Hive / Impala notebook¶
The SQL / Hive / Impala notebook now features a “multi-cells” mechanism that lets you work on several queries at once, without having to juggle several notebooks or search endlessly in the history.
You can also now add comments and descriptions, to transform your SQL notebooks into real data stories.
DSS now includes helper tooltips to guide you through the UI
The Help menu now features a contextual search in our documentation
You can now automatically install the plugin dependencies. Plugin authors can also declare custom installation scripts, if the installation of your plugin is not a simple matter of installing Python or R packages.
New processor for converting “french decimals” (1 234,23) or “US decimals” (1,234.23) to “regular decimals” (1234.23)
New processors to clear cells on number range or with a value
New processors to flag records on number range or with a value
New processor to count occurences of a string or pattern within a cell
Added support of full names in “US State” meaning
Added more mass actions
(Hortonworks only) Tez sessions are now reused in the SQL notebook
Coefficients are now displayed in Ordinary Least Squares regression
Support for ElasticSearch 2.X
Experimental support for append mode on Elasticsearch
Shell: now supports variables
Split: can now split on the result of a formula
Can now define custom additional configuration and statements in “Visual recipes on SQL / Hive / Impala”
The public API now includes a set of methods to interact with user-defined meanings.
R API: now automatically handles lists in resulting dataframes
Infrastructure / Packaging¶
Environment ulimit is now checked when starting
DSS now checks whether server ports are busy at startup
Counting records on SQL query datasets is now possible
MongoDB: dates support has been fixed
MySQL: fixed handling of null dates
MongoDB empty columns are now properly shown
Currency converter now works properly with input date columns
“Step preview” doesn’t change output dataset schema anymore
Copying a preparation recipe with a Join step now generates proper Flow
Formula dates support has been improved
Fixed sort by cardinality in columns view
Hidden clustering buttons in explore view
Exporting “Grouped XY” charts to Excel has been fixed
Fixed issues on charts created by a “Deploy script”
The hproxy process now starts properly if the Hive metastore is unreachable
After a metastore failure, the hproxy now recovers properly
Partitioned recipes on Impala engine have been fixed
Fixed an UI bug on confusion matrix
Managed Folders properly appear in search results
Grouping: drag and drop when 0 keys has been fixed
Stack: “schema union” mode now works properly on Vertica
Window: fixed lead/lag on dates in Vertica
Don’t accept to run a failing join recipe on filesystem datasets with quotes in columns
Fixed various bugs related to Abort in SQL notebooks
Fixed code samples in SQL notebooks
Project admin permission now properly grants all other project permissions
R API: now displays a proper error when trying to write non-existent dataset
R API: fixed writing of data.table object
Infrastructure / Packaging¶
Java startup options are now properly set on all processes
DSS now works properly when you have an http_proxy environment variable